ML Deployment on AWS: From Notebook to Production

Building a model is easy. But, making it usable? Well, that is the real skill.

You can:

• Train models
• Tune accuracy
• Run notebooks

But here’s the gap most learners will never cross:

• How do you turn that model into a real application people can use?

This masterclass shows exactly that: how to take your ML model and deploy it end-to-end on AWS.

Why AWS for Machine Learning?

At the beginning of the session:

• 00:00:06, the focus is clear → ML deployment on AWS
• At 00:01:21, AWS components and architecture are introduced

AWS gives you:

• Infrastructure
• Storage
• Deployment
• API layer

Everything you needed to move from notebook → production.

The Real Problem that Most People Ignore This

• At 00:02:03, a key insight is shared: Most people know model building…But NOT deployment.

That’s where: MLOps Engineers = high demand

Because companies don’t care about:

❌ Notebook models but
✅ Production systems

End-to-End ML Deployment Workflow on AWS

Let’s break down the exact architecture used.

Core AWS Components (The 3 Pillars)

1. AWS SageMaker (The Brain)

• At 00:03:19, SageMaker is introduced

This is where you will build models, train models and Run Jupyter notebooks. This is where Everything starts.

2. Amazon S3 (Storage Layer)

At 00:04:28, the model storage is explained which is used for:

• Storing .pkl models
• Hosting UI files
• Managing datasets

Just think of it as your cloud storage system.

3. AWS Lambda (The Bridge)

At 00:05:29, Lambda is introduced and this is powerful:

It creates your REST API

What it does:

• Connects UI ↔ ML model
• Handles requests
• Scales automatically

Complete Workflow (Big Picture)

Here’s what actually happens:

User → UI → Lambda → SageMaker → Model → Prediction → Response

This is a real-world ML architecture.

Step-by-Step Implementation

Step 1: Create AWS Account

At 00:14:21, AWS setup begins. You’ll need:

• Credit card (for verification)
• Free credits (~$100–$200)

Step 2: Train Model in SageMaker

At 00:10:43, training starts

Steps:

• Upload dataset
• Preprocess data
• Train model (linear regression)

Step 3: Save Model (.pkl → .tar.gz)

At 00:20:52, model saving is explained. Important update: AWS now requires .tar.gz format

Step 4: Upload Model to S3

At 00:22:04, the S3 upload happens. Why? Because models can’t stay on local systems. They must be Stored, Accessible and Scalable

Step 5: Deploy Model to Endpoint

At 00:23:47, the endpoint deployment happens. This makes your model “live”.

Step 6: Create Inference Script

At 00:24:48, inference logic is defined. This script loads the model and handles predictions.

Step 7: Create REST API using Lambda

At 00:25:33, Lambda setup begins. Then what happens:

• API receives input
• Sends a request to the model
• Returns prediction

This is your backend system.

Step 8: Build UI (Frontend Layer)

At 00:31:00, UI creation is shown using HTML / CSS / JS

User enters:

• Square feet
• Bedrooms
• Bathrooms
• Year built

Step 9: Make Application Global (S3 Hosting)

At 00:33:10, the global deployment is done. Then upload UI to S3.

Now:

• Anyone can access your app
• Not just localhost

How Prediction Works (Behind the Scenes)

Here’s what happens when the user clicks “Predict”:

• Input entered in UI
• Request sent to Lambda
• Lambda calls SageMaker endpoint
• Model predicts output
• Result returned to UI

All happens within seconds.

Want to Build This Yourself?

Watching is not enough. If you want to:

• Deploy ML models on AWS
• Build real APIs
• Create production-ready apps

👉 Join AgileFever’s 100% live AI and MLOps Bootcamp with hands-on projects and a capstone.